Results 71 to 80 of about 14,058 (161)

Accurate temperature diagnostics for matter under extreme conditions. [PDF]

open access: yesNat Commun, 2022
Dornheim T   +6 more
europepmc   +1 more source

Regularized Gradient Statistics Improve Generative Deep Learning Models of Super Resolution Microscopy

open access: yesSmall Methods, EarlyView.
A generative AI model for super‐resolution microscopy images is presented. Super‐resolution microscopy provides high spatial detail at the expense of lower time resolution. Using it for live samples requires computational image reconstruction. It is unclear what good priors and metrics for AI‐generated super‐resolution images are.
Meri Abgaryan   +5 more
wiley   +1 more source

Design, Mechanisms, and Applications of DNA‐Mediated Dynamically Reconfigurable Plasmonic Gold Nanostructures

open access: yesSmall Methods, EarlyView.
This review provides key principles of designing and synthesizing reconfigurable plasmonic gold nanostructures for generating and controlling physical, chemical, and biological properties and functions. The recent advances in the biological and materials applications of dynamically reconfigurable plasmonic gold nanostructures are also summarized ...
So Young Choi   +6 more
wiley   +1 more source

Femtoliter Batch Reactors for Nanofluidic Scattering Spectroscopy Analysis of Catalytic Reactions on Single Nanoparticles

open access: yesSmall Methods, EarlyView.
A transient 4.8 femtoliter batch reactor is created within a micro‐ and nanofluidic system with the help of symmetric N2‐streams. The catalytic activity of a single lithographic gold nanoparticle in the reactor is monitored via nanofluidic scattering spectroscopy (NSS), revealing dynamic chemical changes of Fluorescein in the presence of NaBH4 as a ...
Björn Altenburger   +2 more
wiley   +1 more source

Evaluating Multi‐Label Machine Learning Models for Smart Home Environments

open access: yesSoftware: Practice and Experience, EarlyView.
ABSTRACT Context Smart home devices have become increasingly popular in modern households, powered by the Internet of Things (IoT) advances. The data generated by smart devices can provide valuable insights into users' behavior and preferences. By analyzing the data, one can understand how people interact with their homes, thus creating a “smart home ...
Diego Corrêa da Silva   +10 more
wiley   +1 more source

Pseudo-fractional differential equations and generalized g-Laplace transform. [PDF]

open access: yesJ Pseudodiffer Oper Appl, 2021
Sousa JVDC   +3 more
europepmc   +1 more source

Enhancing generalized spectral clustering with embedding Laplacian graph regularization

open access: yesCAAI Transactions on Intelligence Technology, EarlyView.
Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang   +5 more
wiley   +1 more source

Multiphysics Simulation Methods in Computer Graphics

open access: yesComputer Graphics Forum, EarlyView.
Abstract Physics simulation is a cornerstone of many computer graphics applications, ranging from video games and virtual reality to visual effects and computational design. The number of techniques for physically‐based modeling and animation has thus skyrocketed over the past few decades, facilitating the simulation of a wide variety of materials and ...
Daniel Holz   +5 more
wiley   +1 more source

A Stochastic Tree for Bubble Asset Modelling and Pricing

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We introduce a new stochastic tree representation of a strictly stationary submartingale process for modelling, forecasting, and pricing speculative bubbles on commodity and cryptocurrency markets. The model is compared to other trees proposed in the literature on bubble asset modelling and stochastic volatility approximation. We show that the
Christian Gourieroux, Joann Jasiak
wiley   +1 more source

Markov Determinantal Point Process for Dynamic Random Sets

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
wiley   +1 more source

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